IJERT-A Report on Registration Problems in Augmented Reality (original) (raw)

Vision-based Registration for Augmented Reality - A Short Survey

IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2015

The purpose of this paper is to explore some existing techniques in vision-based registration for Augmented Reality (AR) and present them collectively. AR is a branch of computer vision which generally overlays Virtual Objects (VOs) on actual images of real-world scenes in order to provide additional information about the scene to the user. Due to its wide range of applications in the fields of medical, robotics and automotive, geographic and remote sensing, military and aerospace, it has gained high demand. In any AR system, registration is the key to make the augmented scene appearing natural. Registration process must avoid occlusion of VOs and objects in the real world and align the VOs precisely. Optics-based and video-based are two well-known industrial AR systems. Researchers show that even with a single camera model registration for an AR is plausible but, VOs may be registered in front of real-world objects. It is because the registration process lacks depth information of the scene. However, employing stereo vision system and utilizing available natural features in a real-world scene and set of arbitrary multiple planes one can improve accuracy of VO registration. Thus, an AR system becomes robust if it is devised with algorithms to extract and track natural features in real-time.

Linear Augmented Reality Registration

Lecture Notes in Computer Science, 2001

Augmented reality requires the geometric registration of virtual or remote worlds with the visual stimulus of the user. This registration can be achieved by tracking the head pose of the user with respect to the reference coordinate system of the virtual objects. If tracking is achieved with headmounted cameras, registration becomes pose estimation as it is known in computer vision. Augmented reality is by definition a real-time problem, so we are interested only in bounded and short computational time. We propose a new linear algorithm for pose estimation. The algorithm shows a better performance than the linear algorithm by Quan and Lan and is comparable to the non-predicted time iterative algorithm by Kumar and Hanson.

Visual registration for unprepared augmented reality environments

Personal and Ubiquitous …, 2003

Despite the increasing sophistication of augmented reality (AR) tracking technology, tracking in unprepared environments still remains an enormous challenge according to a recent survey. Most current systems are based on a calculation of the optical flow between the current and previous frames to adjust the label position. Here we present two alternative algorithms based on geometrical image constraints. The first is based on epipolar geometry and provides a general description of the constraints on image flow between two static scenes. The second is based on the calculation of a homography relationship between the current frame and a stored representation of the scene. A homography can exactly describe the image motion when the scene is planar, or when the camera movement is a pure rotation, and provides a good approximation when these conditions are nearly met. We assess all three styles of algorithms with a number of criteria including robustness, speed and accuracy. We demonstrate two real-time AR systems here, which are based on the estimation of homography. One is an outdoor geographical labelling/ overlaying system, and the other is an AR Pacman game application.

Computer-vision-based registration techniques for augmented reality

1996

Augmented reality is a term used to describe systems in which computer-generated information is superimposed on top of the real world; for example, through the use of a see-through head-mounted display. A human user of such a system could still see and interact with the real world, but have valuable additional information, such as descriptions of important features or instructions for performing physical tasks, superimposed on the world. For example, the computer could identify objects and overlay them with graphic outlines, labels, and schematics. The graphics are registered to the real-world objects and appear to be "painted" onto those objects. Augmented reality systems can be used to make productivity aids for tasks such as inspection, manufacturing, and navigation.

Calibration Errors in Augmented Reality: A Practical Study

2005

This paper confronts some theoretical camera models to reality and evaluates the suitability of these models for effective augmented reality (AR). It analyses what level of accuracy can be expected in real situations using a particular camera model and how robust the results are against realistic calibration errors. An experimental protocol is used that consists of taking images of a particular scene from different quality cameras mounted on a 4DOF micro-controlled device. The scene is made of a calibration target and three markers placed at different distances of the target. This protocol enables us to consider assessment criteria specific to AR as alignment error and visual impression, in addition to the classical camera positioning error.

Temporal Registration using a Kalman Filter for Augmented Reality Applications

Augmented Reality uses see-through head-mounted displays to superimpose synthetically generated information on a three-dimensional scene. Information is rendered in alignment with physical objects to enhance the user's ability to perceive and interact with the world. A significant technical challenge related to Augmented Reality is determining the transformation that will correctly align the synthetic data with corresponding physical objects. This is made more difficult by the fact that the user and the scene may be in motion. Alignment must be both stable and accurate in order to produce the "illusion" that synthetic objects are an integral part of the environment. This paper presents a tracking algorithm capable of computing the three-dimensional pose of objects with respect to a headmounted camera as they are moved in the scene. Using the fixed transform from the headmounted camera and the see-through device, information can then be displayed in alignment with the current view of the object being tracked. In contrast to approaches that solve for the absolute position of the viewer within a fixed geometry , this object-centered approach to Augmented Reality models motion of both the object and the camerasdisplay system mounted on the user's head using a single transformation involving six parameters.

Registration Using Natural Features for Augmented Reality Systems

IEEE Transactions on Visualization and Computer Graphics, 2006

Registration is one of the most difficult problems in augmented reality (AR) systems. In this paper, a simple registration method using natural features based on the projective reconstruction technique is proposed. This method consists of two steps: embedding and rendering. Embedding involves specifying four points to build the world coordinate system on which a virtual object will be superimposed. In rendering, the Kanade-Lucas-Tomasi (KLT) feature tracker is used to track the natural feature correspondences in the live video. The natural features that have been tracked are used to estimate the corresponding projective matrix in the image sequence. Next, the projective reconstruction technique is used to transfer the four specified points to compute the registration matrix for augmentation. This paper also proposes a robust method for estimating the projective matrix, where the natural features that have been tracked are normalized (translation and scaling) and used as the input data. The estimated projective matrix will be used as an initial estimate for a nonlinear optimization method that minimizes the actual residual errors based on the Levenberg-Marquardt (LM) minimization method, thus making the results more robust and stable. The proposed registration method has three major advantages: 1) It is simple, as no predefined fiducials or markers are used for registration for either indoor and outdoor AR applications. 2) It is robust, because it remains effective as long as at least six natural features are tracked during the entire augmentation, and the existence of the corresponding projective matrices in the live video is guaranteed. Meanwhile, the robust method to estimate the projective matrix can obtain stable results even when there are some outliers during the tracking process.

An adaptive estimator for registration in augmented reality

1999

In augmented reality (AR) systems using headmounted displays (HMD's), it is important to accurately sense the position and orientation (pose) of the user's head with respect to the world, in order that graphical overlays are drawn correctly aligned with real world objects. It is desired to maintain registration dynamically (while the person is moving their head) so that the graphical objects will not appear to lag behind, or swim around, the corresponding real objects. We present an adaptive method for achieving dynamic registration which accounts for variations in the magnitude of the users head motion, based on a multiple model approach. This approach uses the extended Kalman filter to smooth sensor data and estimate position and orientation.

Calibration Requirements and Procedures for Augmented Reality

1997

Augmented reality entails the use of models and their associated renderings to supplement information in a real scene. In order for this information to be relevant or meaningful, the models must be positioned and displayed in such a way that they blend into the real world in terms of alignments, perspectives, illuminations, etc. For practical reasons the information necessary to obtain this realistic blending cannot be known a priori, and cannot be hard-wired into a system. Instead a number of calibration procedures are necessary so that the location and parameters of each of the system components are known. In this paper we identify the calibration steps necessary to build a complete computer model of the real world and then, using the augmented reality system developed at ECRC (Grasp) as an example, we describe each of the calibration processes.

Calibration Method For An Augmented Reality System

2008

In geometrical camera calibration, the objective is to determine a set of camera parameters that describe the mapping between 3D references coordinates and 2D image coordinates. In this paper, a technique of calibration and tracking based on both a least squares method is presented and a correlation technique developed as part of an augmented reality system. This approach is fast and it can be used for a real time system